Www dc 2013

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  • Good day! My name is …, I am from Eindhoven university of Technology, My supervisor is MykolaPechenizkiy.I am happy present.
  • Let’s define what WPA is. Def on slideApplication of WPA on slide, make example – personalized search result page
  • Let’s consider a simple design of web predictive analytics.In WPA we commonly use the following formulation: ON SLIDE to solve WPA tasks.
  • Let’s consider concrete example of user’ next action prediction.How we can predict next user’ action?We have user’ web log. The most commonly used model for sequence prediction is Markov Models.
  • We can process our historical data and build a user navigation graph. Which is suitable to train a markov models.
  • Let’s consider what is a context. ON THE SLIDE
  • Many taxonomies were built for explictit context.Physical EnvironmentFactors -> Conditions -> Weather -> Context can have different grannularity
  • The question what kind of context could be useful for WPA and how we can utilize it.Our research aims to develop a generic framework and corresponding techniques for introducing the contextual information in Predictive Web Analytics and accounting for the practical needs within the considered application areas.
  • Main Resear question on the slideMain focus of my work is to define and discovery useful context and find ways to integrate this context.
  • We can process our historical data and build a user navigation graph. Which is suitable to train a markov models.We can incorporate context to build local markov models which is easy to train and parametrized.
  • Let’s consider the following exampleExplain how it can be helpful
  • In order to build a local Markov models we can partiotion our dat.There are two types of partitioning: horizontal and vertical. Let’s start from horizontal. As context we use “user geo location”.
  • Vertical partition based on user intent
  • We have defined a context as “user intent”, which help us to make a vertical partition.
  • How we can discovery user intent?
  • My focus is building local models.Contextual information Input adjustment, model selection, output adjustment
  • Let’s consider our running example for model selection.
  • More general schema
  • Addчто сделано
  • Лучше убрать
  • Add examples instead of historyRunning examplesAdd definition of explicit and implicit contexts
  • Our research aims to develop a generic framework and corresponding techniques for introducing the contextual information in Predictive Web Analytics and accounting for the practical needs within the considered application areas
  • Www dc 2013

    1. 1. Context mining and integration into Predictive Web Analytics Julia Kiseleva (Eindhoven University of Technology), Supervised by: Mykola Pechenizkiy (Eindhoven University of Technology),
    2. 2. Web Predictive Analytics What is predictive web analytics? Web predictive analytics: • aims to predict individual and aggregated characteristics indicating visitor behavior for purposes of understanding and optimizing web usage. • Application: o Search engines o Recommender System • Examples: o Computational Advertisement • Predictive web analytics tasks: o Online shop’s recommendations; o Users’ next action prediction; o Users’ intention predicting; o Personalized search result page.
    3. 3. Model L Users web log Historical data labels label? 1. training 3. application X y X' y’=L (X') Formulations: ① Classification ② Regression ③ Clustering ④ Scoring labels Testing data 2. testing Predictive Web Analytics
    4. 4. User next action prediction Historical data. Actions ={Search, Refine Search, Click on Banner, Product view, Payment} Search Refine Search ?Click on Banner Product View What is next? Session 1 Search Refine Search Click on Banner Product View Payment Session 3 Product View Payment Session 3 Search Refine Search Refine Search Click on Banner Session 4 Search Refine Search Click on Banner Product View Payment Session 5 Product View Click on Banner Search Running Example: users’ trail predictions
    5. 5. Search Refine Search ?Click on Banner Product View What is next? Running Example: users’ trail predictions Search Refine Search Payment Click on Banner Product View 1.0 2/3 1/3 1/2 1/4 Drop out 3/4 1/4 1 1/4 User next action prediction
    6. 6. Context What is context? – any additional information that Why we need context? o enhances the understanding of the instance of interest, o helps us to classify this instance or makes predictions regarding its behavior. • Two major context types: o Explicit – stored explicitly or given by domain expert (location, OS, Browser) o Implicit – hidden in the data. We need techniques to discover context.
    7. 7. Taxonomy for explicit Context Human Factors Physical Environment Factors User Characteristics Social Environment Intent Conditions Infrastructure Location *Weather *Light *Acceleration *Audio *… *Temperature *Humidity *…
    8. 8. Environment/ Context Model L Users web log X' y' Historical data labels X y label? labels Test data Strategies: ① ? ② ? ③ ? ④ ? Context-Awareness in Web Predictive Analytics
    9. 9. Research Questions Question 1: How to define the context in predictive web analytics? Question 2: How to connect context with the prediction process in predictive web analytics? Context Definition Context Discovery Context Modeling Context Mining: How define context? Context Integration
    10. 10. Search Refine Search ?Click on Banner Product View What is next? Running Example: users’ trail predictions Search Refine Search Payment Click on Banner Product View 1.0 2/3 1/3 1/2 1/4 Drop out 3/4 1/4 1 1/4 User next action prediction
    11. 11. Local models
    12. 12. Contextual Partitioning • Approaches to create local models: o Horizontal partitions Users from Europe Users from South America Session 1 Search Refine Search Click on Banner Product View Payment Session 3 Product View Payment Session 3 Search Refine Search Refine Search Click on Banner Session 4 Search Refine Search Click on Banner Product View Payment Session 5 Product View Click on Banner Search
    13. 13. Contextual Partitioning • Approaches to create local models: o Horizontal partition o Vertical partition : • Two types of behavior: o Ready to by – (Product View, Payment) o Just browsing – (Search, Refine Search, Click on Banner) Session 1 Search Refine Search Click on Banner Product View Payment Session 3 Product View Payment Session 3 Search Refine Search Refine Search Click on Banner Session 4 Search Refine Search Click on Banner Product View Payment Session 5 Product View Click on Banner Search
    14. 14. Contextual Partitioning • Approaches to create local models: o Horizontal partition o Vertical partition : • Two types of behavior: o Ready to by – (Product View, Payment) o Just browsing – (Search, Refine Search, Click on Banner) Session 1 Search Refine Search Click on Banner Product View Payment Session 3 Product View Payment Session 3 Search Refine Search Refine Search Click on Banner Session 4 Search Refine Search Click on Banner Product View Payment Session 5 Product View Click on Banner Search
    15. 15. Context Definition • Intuition about Context: change of user intents o User is looking for the product o User is ready to buy Search Refine Search Payment Click on Banner Product View Intent: looking for product Intent: ready to buy
    16. 16. Context Discovery • Context definition: change of user intents o User is looking for the product o User is ready to buy • Context discovery – apply hierarchical clustering in order to maximize prediction accuracy Search Refine Search PaymentClick Product View Intent: looking for product Intent: ready to buy
    17. 17. Context-Awareness Integration Predictive model(s) PredictionsTraining data Context- awareness Example: Seasonality (winter, summer ) Example: Features set expansion Example: Prediction adjustment
    18. 18. Context Integration Example Context: User intent DATA L1 L2 Contextual Categories Individual Learners Mapping G Mapping H Context Discovery Ready to buy Just browsing
    19. 19. Context Integration Example …………… … C1 C2 C3 Cn Contextual features Fs DATA Environment L1 L2 L3 Lk Contextual Categories Individual Learners Mapping G Mapping H Context Discovery
    20. 20. Thank you! • Context identification and integration it into prediction models • Accurately predicting users’ desired actions and understanding behavioral patterns of users in various web-applications • Personalization and adaptation to diverse customer need and preferences • Accounting for the practical needs within the considered application areas.
    21. 21. Summary • The main goal is to develop a generic framework for context-aware systems for Web Predictive Analytics • In order to archive this goal we need to answer the following questions: o How to define the context in predictive web analytics? o How to connect context with the prediction process in predictive web analytics? Questions?
    22. 22. Research Methodology Implementing CAPA framework Developing CAPA framework Online validation (A/B testing) Internal validation
    23. 23. Context-aware systems Context definition Context Integration Method Application Context-aware system Recommendation systems Computational Advertisement Information Retrieval Normalization Expansion Classifier Selection Classification Adjustment Weighting Domain Expert Clustering Contextual feature identification
    24. 24. History of context definition and discovery Context Year Location 1992 Taxonomy of explicit context 1999 Predictive features vs. contextual 2002 Hidden context: (clustering, mixture models) 2004 Contextual bandits 2007 History of previous interaction 2008 Independence of predicted class 2011 Two level prediction model 2012 Focus on Context Discovery 2012 - Timeline
    25. 25. Research Goal • Our research aims to develop a generic framework and corresponding techniques for introducing the contextual information in Predictive Web Analytics and accounting for the practical needs within the considered application areas.
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